The Changing Nature of “Calls” for Help with Hurricane Harvey: Comparing 9-1-1 and Social Media
This project is funded by Award#1760453 of the National Science Foundation’s Division Of Computer and Network Systems . Hurricane Harvey is the first big-data disaster where social media “calls” for help appear to have supplanted the overloaded 9-1-1 call systems; social media provided a visible, dialogic link to help. But this form of help-seeking behavior on public social media is relatively new. This project (1) captures the voices of hurricane victims and emergency response workers (both governmental and volunteer) (2) uses captured data to characterize the language present in actual social media calls for help, and (3) applies a big-data approach to a new emergency situation to assess that situation’s calls for help. This project paves the way for new ways of thinking about how first-responders can utilize social media alongside traditional 9-1-1 when dispatching in future emergencies. Dr. Murthy is Co-PI on this project.
Venmo: Understanding Mobile Payments as Social Media
Payment infrastructures are going through rapid change with the rise of next generation mobile networks and smartphone ownership. From mobile wallets to rideshare apps, social payments allow users to split receipts with friends, charge exes for breakup expenses, or troll celebrities. New layers of data, sociality, and markets are being created and influenced by expanding economic imaginaries, regulations, and business models leveraging these new infrastructures. In this project we discuss how mobile payment systems have become social media. In this project, Drs. Murthy and Acker examine Venmo, a social payments platform that allows users to broadcast transactions to a social activity stream or public transaction feed. Our findings detail how transaction feeds of mobile payments support social practices, communication, and commerce with mobile devices and wireless networks. Our case study on Venmo helps develop some implications for the design, study, and impact of mobile payment infrastructures as social media.
Aubrey O’Neal and Martin Riedl are graduate students that are/have been involved in CML’s Venmo-related research.
Trump on Twitter
One of our initial projects examines 3 million tweets containing the term “Trump” that were shared during the election cycle last year. We’re comparing them to 3 million tweets using the same word collected during President Donald Trump’s first 30 days in office.
These types of large-scale data projects enable us to explore new forms of knowledge that are difficult to glean from non-computational approaches. Social media is the key data source throughout the Computational Media Lab’s research. For this particular project, we’ll not only be studying the sentiment of the tweets and how they may have changed over time; we’ll also be using advanced methods of social network analysis, which allow us to plot out interactions between individuals tweeting with the word “Trump” during both time periods. After that, we’ll tackle large-scale machine learning in order to best explore the topics these tweets represent and examine their importance within the larger social, political, and economic contexts.
Red, White and Screw You
Exploring Facebook comments during the 2016 U.S. presidential election:
This study analyzes political discourse and online commenting behavior surrounding Hillary Clinton’s and Donald Trump’s Facebook pages during the 2016 U.S. presidential election. It proposes a systematic classification method (support vector machines) to classify supporters with the candidates they supported, according to their Facebook comments. Using theoretical concepts from the functional theory of political campaign discourse (Benoit, 1999) — i.e. acclaims, attacks,defenses — this project applies computational content analysis on Facebook comments to examine what qualities supporters praise about their favorite candidate on Facebook. What frames do they use to attack the opposing candidate and his/her supporters? And how do they defend their preferred candidate and his/her beliefs?